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null | null | {} | EvilGirlfriend/Ua | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Evye/Eve | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Ewan1011/phoenixbot-chat | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Ewan1011/phoenixbotchat | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
#jdt chat bot | {"tags": ["conversational"]} | ExEngineer/DialoGPT-medium-jdt | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | ExSol/Alex | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Exelby/Exe | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Exelby/Exelbyexe | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Quirk DialoGPT Model | {"tags": ["conversational"]} | Exilon/DialoGPT-large-quirk | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Exor/DialoGPT-small-harrypotter | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Extreole/test | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | read me | {} | EyeSeeThru/txt2img | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-russian-big-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-russian-big-kaggle", "results": []}]} | Eyvaz/wav2vec2-base-russian-big-kaggle | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-russian-demo-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: inf
- Wer: 0.9997
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0102 | 1.03 | 500 | inf | 0.9997 |
| 0.0068 | 2.06 | 1000 | inf | 0.9997 |
| 0.0 | 3.09 | 1500 | inf | 0.9997 |
| 0.0313 | 4.12 | 2000 | inf | 0.9997 |
| 0.0 | 5.15 | 2500 | inf | 0.9997 |
| 0.0052 | 6.19 | 3000 | inf | 0.9997 |
| 0.0287 | 7.22 | 3500 | inf | 0.9997 |
| 0.0 | 8.25 | 4000 | inf | 0.9997 |
| 0.01 | 9.28 | 4500 | inf | 0.9997 |
| 0.0 | 10.31 | 5000 | inf | 0.9997 |
| 0.3919 | 11.34 | 5500 | inf | 0.9997 |
| 0.0 | 12.37 | 6000 | inf | 0.9997 |
| 0.0 | 13.4 | 6500 | inf | 0.9997 |
| 0.0 | 14.43 | 7000 | inf | 0.9997 |
| 0.6422 | 15.46 | 7500 | inf | 0.9997 |
| 0.0 | 16.49 | 8000 | inf | 0.9997 |
| 0.0 | 17.53 | 8500 | inf | 0.9997 |
| 0.0 | 18.56 | 9000 | inf | 0.9997 |
| 0.0 | 19.59 | 9500 | inf | 0.9997 |
| 0.0 | 20.62 | 10000 | inf | 0.9997 |
| 0.0427 | 21.65 | 10500 | inf | 0.9997 |
| 0.0 | 22.68 | 11000 | inf | 0.9997 |
| 0.0 | 23.71 | 11500 | inf | 0.9997 |
| 0.0 | 24.74 | 12000 | inf | 0.9997 |
| 0.0091 | 25.77 | 12500 | inf | 0.9997 |
| 0.1243 | 26.8 | 13000 | inf | 0.9997 |
| 0.0 | 27.83 | 13500 | inf | 0.9997 |
| 0.0 | 28.87 | 14000 | inf | 0.9997 |
| 0.0 | 29.9 | 14500 | inf | 0.9997 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-base-russian-demo-kaggle", "results": []}]} | Eyvaz/wav2vec2-base-russian-demo-kaggle | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-russian-modified-kaggle
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"]} | Eyvaz/wav2vec2-base-russian-modified-kaggle | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | EzioDD/DialoGPT-small-house | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
#house small GPT | {"tags": ["conversational"]} | EzioDD/house | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | {} | FAN-L/HM_model001 | null | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# FFF dialog model | {"tags": "conversational"} | FFF000/dialogpt-FFF | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers | {} | FFZG-cleopatra/bert-emoji-latvian-twitter | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2169 | 1.0 | 8235 | 1.1950 |
| 0.9396 | 2.0 | 16470 | 1.2540 |
| 0.7567 | 3.0 | 24705 | 1.4306 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad_v2"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | FOFer/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | transformers | {} | FPTAI/velectra-base-discriminator-cased | null | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | transformers | {} | FPTAI/vibert-base-cased | null | [
"transformers",
"pytorch",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Fabby/gpt2-english-light-novel-titles | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers |
# HotelBERT-small
This model was trained on reviews from a well known German hotel platform.
| {"language": "de", "widget": [{"text": "Das <mask> hat sich toll um uns gek\u00fcmmert."}]} | FabianGroeger/HotelBERT-small | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# HotelBERT
This model was trained on reviews from a well known German hotel platform.
| {"language": "de", "widget": [{"text": "Das <mask> hat sich toll um uns gek\u00fcmmert."}]} | FabianGroeger/HotelBERT | null | [
"transformers",
"pytorch",
"tf",
"roberta",
"fill-mask",
"de",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2196
- Accuracy: 0.926
- F1: 0.9258
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8279 | 1.0 | 250 | 0.3208 | 0.9025 | 0.8979 |
| 0.2538 | 2.0 | 500 | 0.2196 | 0.926 | 0.9258 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}, {"type": "f1", "value": 0.9258450981645597, "name": "F1"}]}]}]} | FabioDataGeek/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Faky/DialoGPT-small-RickBot | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Famaral97/distilbert-base-uncased-finetuned-ner | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers | <!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-uncased-base
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an Reddit-dialogue dataset.
This model can be used for Text Classification: Given two sentences, see if they are related.
It achieves the following results on the evaluation set:
- Loss: 0.2297
- Accuracy: 0.9267
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 320
- eval_batch_size: 80
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.11.0
## Usage (HuggingFace Transformers)
You can use the model like this:
```python
import torch
from transformers import AutoModelForSequenceClassification, AutoTokenizer
# label_list
label_list = ['matched', 'unmatched']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("Fan-s/reddit-tc-bert", use_fast=True)
model = AutoModelForSequenceClassification.from_pretrained("Fan-s/reddit-tc-bert")
# Set the input
post = "don't make gravy with asbestos."
response = "i'd expect someone with a culinary background to know that. since we're talking about school dinner ladies, they need to learn this pronto."
# Predict whether the two sentences are matched
def predict(post, response, max_seq_length=128):
with torch.no_grad():
args = (post, response)
input = tokenizer(*args, padding="max_length", max_length=max_seq_length, truncation=True, return_tensors="pt")
output = model(**input)
logits = output.logits
item = torch.argmax(logits, dim=1)
predict_label = label_list[item]
return predict_label, logits
predict_label, logits = predict(post, response)
# Matched
print("predict_label:", predict_label)
``` | {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"]} | Fan-s/reddit-tc-bert | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Fang/Titania | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | @Kirito DialoGPT Small Model | {"tags": ["conversational"]} | FangLee/DialoGPT-small-Kirito | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | FardinSaboori/bert-finetuned-squad-accelerate | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-finetuned-squad", "results": []}]} | FardinSaboori/bert-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | FarhanAli/RoBERT_healthFact | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FarhanAli/health_fact_data_models | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers | {} | FarisHijazi/wav2vec2-large-xls-r-300m-arabic-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-turkish-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 256
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu113
- Datasets 1.13.3
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-turkish-colab", "results": []}]} | FarisHijazi/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | FarisHijazi/wav2vec2-large-xlsr-turkish-demo-colab | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Farjami/Modal1 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Fatemah/salamBERT | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-classification | transformers |
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 32517788
- CO2 Emissions (in grams): 0.9413042739759596
## Validation Metrics
- Loss: 0.32112351059913635
- Accuracy: 0.8641304347826086
- Precision: 0.8055555555555556
- Recall: 0.8405797101449275
- AUC: 0.9493383742911153
- F1: 0.8226950354609929
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/Fauzan/autonlp-judulberita-32517788
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("Fauzan/autonlp-judulberita-32517788", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` | {"language": "unk", "tags": "autonlp", "datasets": ["Fauzan/autonlp-data-judulberita"], "widget": [{"text": "I love AutoNLP \ud83e\udd17"}], "co2_eq_emissions": 0.9413042739759596} | Fauzan/autonlp-judulberita-32517788 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"autonlp",
"unk",
"dataset:Fauzan/autonlp-data-judulberita",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | FelipeV/bert-base-spanish-uncased-sentiment | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | This model was fine-tuned to generate horror stories in a collaborative way.
Check it out on our [repo](https://github.com/TailUFPB/storIA). | {} | Felipehonorato/storIA | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1495
- Accuracy: 0.9385
- F1: 0.9383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1739 | 1.0 | 250 | 0.1827 | 0.931 | 0.9302 |
| 0.1176 | 2.0 | 500 | 0.1567 | 0.9325 | 0.9326 |
| 0.0994 | 3.0 | 750 | 0.1555 | 0.9385 | 0.9389 |
| 0.08 | 4.0 | 1000 | 0.1496 | 0.9445 | 0.9443 |
| 0.0654 | 5.0 | 1250 | 0.1495 | 0.9385 | 0.9383 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.18.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9385, "name": "Accuracy"}, {"type": "f1", "value": 0.9383492808338979, "name": "F1"}]}]}]} | Fengkai/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Fengkai/xlm-roberta-base-finetuned-panx-de | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Fenshee/Tania | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Fera/HakaiMono | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# GPT2-SMALL-PORTUGUESE-WIKIPEDIABIO
This is a finetuned model version of gpt2-small-portuguese(https://huggingface.co/pierreguillou/gpt2-small-portuguese) by pierreguillou.
It was trained on a person abstract dataset extracted from DBPEDIA (over 100000 people's abstracts). The model is intended as a simple and fun experiment for generating texts abstracts based on ordinary people's names. | {"language": "pt", "tags": ["pt", "wikipedia", "gpt2", "finetuning"], "datasets": ["wikipedia"], "widget": ["Andr\u00e9 Um", "Maria do Santos", "Roberto Carlos"], "licence": "mit"} | Ferch423/gpt2-small-portuguese-wikipediabio | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"pt",
"wikipedia",
"finetuning",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Ferial/distilbert-base-uncased-finetuned-ner | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Ferran/pk-bert | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | espnet |
## ESPnet2 ASR model
### `Fhrozen/test_an4`
This model was trained by Fhrozen using an4 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b8df4c928e132acff78d196988bdb68a66987952
pip install -e .
cd egs2/an4/asr1
./run.sh --skip_data_prep false --skip_train true --download_model Fhrozen/test_an4
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Wed Oct 20 00:00:46 JST 2021`
- python version: `3.9.7 (default, Sep 16 2021, 13:09:58) [GCC 7.5.0]`
- espnet version: `espnet 0.10.4a1`
- pytorch version: `pytorch 1.9.0`
- Git hash: `b8df4c928e132acff78d196988bdb68a66987952`
- Commit date: `Tue Oct 19 07:48:11 2021 -0400`
## asr_train_raw_en_bpe30
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|773|4.0|22.3|73.7|0.1|96.1|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|591|2.7|21.8|75.5|0.0|97.3|100.0|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2565|17.2|16.4|66.4|1.0|83.8|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|1915|15.5|16.4|68.1|0.9|85.5|100.0|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/test|130|2695|21.1|15.6|63.3|0.9|79.9|100.0|
|inference_lm_lm_train_lm_en_bpe30_valid.loss.ave_asr_model_valid.acc.best/train_dev|100|2015|19.4|15.6|65.0|0.9|81.5|100.0|
## ASR config
<details><summary>expand</summary>
```
config: null
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_raw_en_bpe30
ngpu: 0
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 20
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe30/train/speech_shape
- exp/asr_stats_raw_en_bpe30/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe30/valid/speech_shape
- exp/asr_stats_raw_en_bpe30/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_nodev/wav.scp
- speech
- sound
- - dump/raw/train_nodev/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev/wav.scp
- speech
- sound
- - dump/raw/train_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf: {}
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- T
- E
- O
- R
- Y
- A
- H
- U
- S
- I
- F
- B
- L
- P
- D
- G
- M
- C
- V
- X
- J
- K
- Z
- W
- N
- Q
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
model_conf:
ctc_weight: 0.5
ignore_id: -1
lsm_weight: 0.0
length_normalized_loss: false
report_cer: true
report_wer: true
sym_space: <space>
sym_blank: <blank>
extract_feats_in_collect_stats: true
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram30/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: null
specaug_conf: {}
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_en_bpe30/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: rnn
encoder_conf: {}
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf: {}
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
## LM config
<details><summary>expand</summary>
```
config: conf/train_lm.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/lm_train_lm_en_bpe30
ngpu: 0
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 40
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- loss
- min
keep_nbest_models: 1
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 256
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/lm_stats_en_bpe30/train/text_shape.bpe
valid_shape_file:
- exp/lm_stats_en_bpe30/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/lm_train.txt
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/train_dev/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- T
- E
- O
- R
- Y
- A
- H
- U
- S
- I
- F
- B
- L
- P
- D
- G
- M
- C
- V
- X
- J
- K
- Z
- W
- N
- Q
- <sos/eos>
init: null
model_conf:
ignore_id: 0
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram30/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
lm: seq_rnn
lm_conf:
unit: 650
nlayers: 2
required:
- output_dir
- token_list
version: 0.10.4a1
distributed: false
```
</details>
| {"language": "en", "license": "cc-by-4.0", "tags": ["espnet", "audio", "automatic-speech-recognition"], "datasets": ["an4"]} | Fhrozen/test_an4 | null | [
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:an4",
"license:cc-by-4.0",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.9291
- Recall: 0.9376
- F1: 0.9333
- Accuracy: 0.9841
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2412 | 1.0 | 878 | 0.0688 | 0.9178 | 0.9246 | 0.9212 | 0.9815 |
| 0.0514 | 2.0 | 1756 | 0.0608 | 0.9251 | 0.9344 | 0.9298 | 0.9832 |
| 0.0304 | 3.0 | 2634 | 0.0604 | 0.9291 | 0.9376 | 0.9333 | 0.9841 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.12.1
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["conll2003"], "metrics": ["precision", "recall", "f1", "accuracy"], "model-index": [{"name": "distilbert-base-uncased-finetuned-ner", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "conll2003", "type": "conll2003", "args": "conll2003"}, "metrics": [{"type": "precision", "value": 0.9290544285555925, "name": "Precision"}, {"type": "recall", "value": 0.9375769101689228, "name": "Recall"}, {"type": "f1", "value": 0.9332962138084633, "name": "F1"}, {"type": "accuracy", "value": 0.9841136193940935, "name": "Accuracy"}]}]}]} | Fiddi/distilbert-base-uncased-finetuned-ner | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Fiftyzed/Fiftyzed | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
token-classification | transformers | {} | Film8844/wangchanberta-ner | null | [
"transformers",
"pytorch",
"camembert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FilmonK/DialoGPT-small-harrypotter | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# updated PALPATINE DialoGPT Model | {"tags": ["conversational"]} | Filosofas/DialoGPT-medium-PALPATINE | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Finka/model_name | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
feature-extraction | transformers |
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification. The ConvBERT generator model intented to be used for fill-mask task is released here [Finnish-NLP/convbert-base-generator-finnish](https://huggingface.co/Finnish-NLP/convbert-base-generator-finnish)
## Model description
Finnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ConvBERT model as inputs.
Compared to BERT and ELECTRA models, ConvBERT model utilizes a span-based
dynamic convolution to replace some of the global self-attention heads for modeling local input sequence
dependencies. These convolution heads, together with the rest of the self-attention
heads, form a new mixed attention block that should be more efficient at both global
and local context learning.
## Intended uses & limitations
You can use the raw model for extracting features or fine-tune it to a downstream task like text classification.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import ConvBertTokenizer, ConvBertModel
import torch
tokenizer = ConvBertTokenizer.from_pretrained("Finnish-NLP/convbert-base-finnish")
model = ConvBertModel.from_pretrained("Finnish-NLP/convbert-base-finnish")
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="pt")
outputs = model(**inputs)
print(outputs.last_hidden_state)
```
and in TensorFlow:
```python
from transformers import ConvBertTokenizer, TFConvBertModel
tokenizer = ConvBertTokenizer.from_pretrained("Finnish-NLP/convbert-base-finnish")
model = TFConvBertModel.from_pretrained("Finnish-NLP/convbert-base-finnish")
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="tf")
outputs = model(inputs)
print(outputs.last_hidden_state)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ConvBERT model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ConvBERT repository](https://github.com/yitu-opensource/ConvBert) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/convbert/CHEATSHEET.md).
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our other models:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|-----------------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/convbert-base-finnish |86.98 |94.04 |95.02 |71.87 |
|Finnish-NLP/electra-base-discriminator-finnish |86.25 |93.78 |94.77 |70.20 |
|Finnish-NLP/roberta-large-wechsel-finnish |88.19 |**94.91** |95.18 |74.47 |
|Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |94.90 |**95.49** |**76.07** |
To conclude, this ConvBERT model wins the ELECTRA model while losing to other models but is still fairly competitive compared to our roberta-large models when taking into account that this ConvBERT model has 106M parameters when roberta-large models have 355M parameters. ConvBERT winning the ELECTRA is also in line with the findings of the [ConvBERT paper](https://arxiv.org/abs/2008.02496).
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "convbert"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"]} | Finnish-NLP/convbert-base-finnish | null | [
"transformers",
"pytorch",
"tf",
"tensorboard",
"convbert",
"feature-extraction",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2008.02496",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# ConvBERT for Finnish
Pretrained ConvBERT model on Finnish language using a replaced token detection (RTD) objective. ConvBERT was introduced in
[this paper](https://arxiv.org/abs/2008.02496)
and first released at [this page](https://github.com/yitu-opensource/ConvBert).
**Note**: this model is the ConvBERT generator model intented to be used for the fill-mask task. The ConvBERT discriminator model intented to be used for fine-tuning on downstream tasks like text classification is released here [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish)
## Model description
Finnish ConvBERT is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ConvBERT model as inputs.
Compared to BERT and ELECTRA models, ConvBERT model utilizes a span-based
dynamic convolution to replace some of the global self-attention heads for modeling local input sequence
dependencies. These convolution heads, together with the rest of the self-attention
heads, form a new mixed attention block that should be more efficient at both global
and local context learning.
## Intended uses & limitations
You can use this generator model mainly just for the fill-mask task. For other tasks, check the [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish) model instead.
### How to use
Here is how to use this model directly with a pipeline for fill-mask task:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/convbert-base-generator-finnish')
>>> unmasker("Moikka olen [MASK] kielimalli.")
[{'score': 0.08341152966022491,
'token': 4619,
'token_str': 'suomalainen',
'sequence': 'Moikka olen suomalainen kielimalli.'},
{'score': 0.02831297740340233,
'token': 25583,
'token_str': 'ranskalainen',
'sequence': 'Moikka olen ranskalainen kielimalli.'},
{'score': 0.027857203036546707,
'token': 37714,
'token_str': 'kiinalainen',
'sequence': 'Moikka olen kiinalainen kielimalli.'},
{'score': 0.027701903134584427,
'token': 21614,
'token_str': 'ruotsalainen',
'sequence': 'Moikka olen ruotsalainen kielimalli.'},
{'score': 0.026388710364699364,
'token': 591,
'token_str': 'hyvä',
'sequence': 'Moikka olen hyvä kielimalli.'}]
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ConvBERT model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ConvBERT repository](https://github.com/yitu-opensource/ConvBert) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/convbert/CHEATSHEET.md).
## Evaluation results
For evaluation results, check the [Finnish-NLP/convbert-base-finnish](https://huggingface.co/Finnish-NLP/convbert-base-finnish) model repository instead.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "convbert"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen [MASK] kielimalli."}]} | Finnish-NLP/convbert-base-generator-finnish | null | [
"transformers",
"pytorch",
"convbert",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:2008.02496",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | transformers |
# ELECTRA for Finnish
Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
[this paper](https://openreview.net/pdf?id=r1xMH1BtvB)
and first released at [this page](https://github.com/google-research/electra).
**Note**: this model is the ELECTRA discriminator model intented to be used for fine-tuning on downstream tasks like text classification. The ELECTRA generator model intented to be used for fill-mask task is released here [Finnish-NLP/electra-base-generator-finnish](https://huggingface.co/Finnish-NLP/electra-base-generator-finnish)
## Model description
Finnish ELECTRA is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ELECTRA model as inputs.
## Intended uses & limitations
You can use the raw model for extracting features or fine-tune it to a downstream task like text classification.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import ElectraTokenizer, ElectraModel
import torch
tokenizer = ElectraTokenizer.from_pretrained("Finnish-NLP/electra-base-discriminator-finnish")
model = ElectraModel.from_pretrained("Finnish-NLP/electra-base-discriminator-finnish")
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="pt")
outputs = model(**inputs)
print(outputs.last_hidden_state)
```
and in TensorFlow:
```python
from transformers import ElectraTokenizer, TFElectraModel
tokenizer = ElectraTokenizer.from_pretrained("Finnish-NLP/electra-base-discriminator-finnish")
model = TFElectraModel.from_pretrained("Finnish-NLP/electra-base-discriminator-finnish", from_pt=True)
inputs = tokenizer("Joka kuuseen kurkottaa, se katajaan kapsahtaa", return_tensors="tf")
outputs = model(inputs)
print(outputs.last_hidden_state)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ELECTRA model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 2e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ELECTRA repository](https://github.com/google-research/electra) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/electra/CHEATSHEET.md).
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our other models:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|-----------------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/electra-base-discriminator-finnish |86.25 |93.78 |94.77 |70.20 |
|Finnish-NLP/convbert-base-finnish |86.98 |94.04 |95.02 |71.87 |
|Finnish-NLP/roberta-large-wechsel-finnish |88.19 |**94.91** |95.18 |74.47 |
|Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |94.90 |**95.49** |**76.07** |
To conclude, this ELECTRA model loses to other models but is still fairly competitive compared to our roberta-large models when taking into account that this ELECTRA model has 110M parameters when roberta-large models have 355M parameters.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "electra"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"]} | Finnish-NLP/electra-base-discriminator-finnish | null | [
"transformers",
"pytorch",
"tensorboard",
"electra",
"pretraining",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# ELECTRA for Finnish
Pretrained ELECTRA model on Finnish language using a replaced token detection (RTD) objective. ELECTRA was introduced in
[this paper](https://openreview.net/pdf?id=r1xMH1BtvB)
and first released at [this page](https://github.com/google-research/electra).
**Note**: this model is the ELECTRA generator model intented to be used for the fill-mask task. The ELECTRA discriminator model intented to be used for fine-tuning on downstream tasks like text classification is released here [Finnish-NLP/electra-base-discriminator-finnish](https://huggingface.co/Finnish-NLP/electra-base-discriminator-finnish)
## Model description
Finnish ELECTRA is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the replaced token detection (RTD) objective. Instead of masking the input like in BERT's masked language modeling (MLM) objective, this approach corrupts the input by replacing some tokens with plausible alternatives sampled from a small generator model. Then, instead of training a model that predicts the original identities of the corrupted tokens, a discriminative model is trained that predicts whether each token in the corrupted input was replaced by a generator model's sample or not. Thus, this training approach resembles Generative Adversarial Nets (GAN).
This way, the model learns an inner representation of the Finnish language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the ELECTRA model as inputs.
## Intended uses & limitations
You can use this generator model mainly just for the fill-mask task. For other tasks, check the [Finnish-NLP/electra-base-discriminator-finnish](https://huggingface.co/Finnish-NLP/electra-base-discriminator-finnish) model instead.
### How to use
Here is how to use this model directly with a pipeline for fill-mask task:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/electra-base-generator-finnish')
>>> unmasker("Moikka olen [MASK] kielimalli.")
[{'score': 0.0708453431725502,
'token': 4619,
'token_str': 'suomalainen',
'sequence': 'Moikka olen suomalainen kielimalli.'},
{'score': 0.042563650757074356,
'token': 1153,
'token_str': 'uusi',
'sequence': 'Moikka olen uusi kielimalli.'},
{'score': 0.03219178691506386,
'token': 591,
'token_str': 'hyvä',
'sequence': 'Moikka olen hyvä kielimalli.'},
{'score': 0.03175133094191551,
'token': 3134,
'token_str': 'vanha',
'sequence': 'Moikka olen vanha kielimalli.'},
{'score': 0.019662367179989815,
'token': 25583,
'token_str': 'ranskalainen',
'sequence': 'Moikka olen ranskalainen kielimalli.'}]
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
## Training data
This Finnish ELECTRA model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using WordPiece and a vocabulary size of 50265. The inputs are sequences of 512 consecutive tokens. Texts are not lower cased so this model is case-sensitive: it makes a difference between finnish and Finnish.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 1M steps. The optimizer used was a AdamW with learning rate 2e-4, learning rate warmup for 20000 steps and linear decay of the learning rate after.
Training code was from the official [ELECTRA repository](https://github.com/google-research/electra) and also some instructions was used from [here](https://github.com/stefan-it/turkish-bert/blob/master/electra/CHEATSHEET.md).
## Evaluation results
For evaluation results, check the [Finnish-NLP/electra-base-discriminator-finnish](https://huggingface.co/Finnish-NLP/electra-base-discriminator-finnish) model repository instead.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "electra"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen [MASK] kielimalli."}]} | Finnish-NLP/electra-base-generator-finnish | null | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# GPT-2 for Finnish
Pretrained GPT-2 model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
**Note**: this model is quite small 117M parameter variant as in Huggingface's [GPT-2 config](https://huggingface.co/gpt2), so not the famous big 1.5B parameter variant by OpenAI. We also have bigger 345M parameter variant [gpt2-medium-finnish](https://huggingface.co/Finnish-NLP/gpt2-medium-finnish) and 774M parameter variant [gpt2-large-finnish](https://huggingface.co/Finnish-NLP/gpt2-large-finnish) available which perform better compared to this model.
## Model description
Finnish GPT-2 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation:
```python
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-finnish')
>>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5)
[{'generated_text': 'Tekstiä tuottava tekoäly on kuin onkin hyvin pieni. Sitä voi käyttää myös hyvin nopeasti ja myös täysin automatisoituna, eikä sitä tarvitse käydä läpi. Se'},
{'generated_text': 'Tekstiä tuottava tekoäly on saanut jalansijaa, mutta Suomessa se on jo ehtinyt hajota käsiin, koska sen avulla ei pystytä tuottamaan täysin ajantasaisia'},
{'generated_text': 'Tekstiä tuottava tekoäly on tehnyt työtä kymmenien vuosien ajan ja ottanut käyttöön jo yli kahden vuosikymmenen ajan tekoälyn ratkaisuja. Tekoäly on jo pitkään tehnyt työtä'},
{'generated_text': 'Tekstiä tuottava tekoäly on tekoälyn sovellus, jota käytetään esimerkiksi liiketoiminnan ja päätöksenteon tukena. Työhön liittyy data-analyysin ohella tekoälyn avulla esimerkiksi tekoäl'},
{'generated_text': 'Tekstiä tuottava tekoäly on juuri nyt erityisen hyödyllinen, koska se tunnistaa käyttäjän tietokoneen ruudulla olevat ilmoitukset, kuten näytön värin ja osoittimet ilman välkyn'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-finnish')
model = GPT2Model.from_pretrained('Finnish-NLP/gpt2-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-finnish')
model = TFGPT2Model.from_pretrained('Finnish-NLP/gpt2-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
As with all language models, it is hard to predict in advance how the Finnish GPT-2 will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Training data
This Finnish GPT-2 model was pretrained on the combination of six datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 300k steps (a bit over 2 epochs, 256 batch size). The optimizer used was a second-order optimization method called [Distributed Shampoo](https://github.com/google-research/google-research/tree/master/scalable_shampoo) with learning rate 1e-4, learning rate warmup for 4000 steps and cosine decay of the learning rate after.
At first, commonly used Adam optimizer was tried but there were significant issues getting the model to converge even with multiple different learning rate trials so then Adam optimizer was replaced with the Distributed Shampoo which worked a lot better.
## Evaluation results
Evaluation was done using the *validation* split of the [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned) dataset with [Perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) loses to our bigger model variants.
| | Perplexity |
|------------------------------------------|------------|
|Finnish-NLP/gpt2-finnish |44.19 |
|Finnish-NLP/gpt2-medium-finnish |34.08 |
|Finnish-NLP/gpt2-large-finnish |**30.74** |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗
| {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# GPT-2 large for Finnish
Pretrained GPT-2 large model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
**Note**: this model is 774M parameter variant as in Huggingface's [GPT-2-large config](https://huggingface.co/gpt2-large), so not the famous big 1.5B parameter variant by OpenAI.
## Model description
Finnish GPT-2 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation:
```python
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-large-finnish')
>>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5)
[{'generated_text': 'Tekstiä tuottava tekoäly on valmis yhteistyöhön ihmisen kanssa: Tekoäly hoitaa ihmisen puolesta tekstin tuottamisen. Se myös ymmärtää, missä vaiheessa tekstiä voidaan alkaa kirjoittamaan'},
{'generated_text': 'Tekstiä tuottava tekoäly on älykäs, mutta se ei ole vain älykkäisiin koneisiin kuuluva älykäs olento, vaan se on myös kone. Se ei'},
{'generated_text': 'Tekstiä tuottava tekoäly on ehkä jo pian todellisuutta - se voisi tehdä myös vanhustenhoidosta nykyistä ä tuottava tekoäly on ehkä jo pian todellisuutta - se voisi tehdä'},
{'generated_text': 'Tekstiä tuottava tekoäly on kehitetty ihmisen ja ihmisen aivoihin yhteistyössä neurotieteiden ja käyttäytymistieteen tutkijatiimin kanssa. Uusi teknologia avaa aivan uudenlaisia tutkimusi'},
{'generated_text': 'Tekstiä tuottava tekoäly on kuin tietokone, jonka kanssa voi elää. Tekoälyn avulla voi kirjoittaa mitä tahansa, mistä tahansa ja miten paljon. Tässä'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-large-finnish')
model = GPT2Model.from_pretrained('Finnish-NLP/gpt2-large-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-large-finnish')
model = TFGPT2Model.from_pretrained('Finnish-NLP/gpt2-large-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
As with all language models, it is hard to predict in advance how the Finnish GPT-2 will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Training data
This Finnish GPT-2 model was pretrained on the combination of six datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 640k steps (a bit over 1 epoch, 64 batch size). The optimizer used was a AdamW with learning rate 4e-5, learning rate warmup for 4000 steps and cosine decay of the learning rate after.
## Evaluation results
Evaluation was done using the *validation* split of the [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned) dataset with [Perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) performs better than our smaller model variants.
| | Perplexity |
|------------------------------------------|------------|
|Finnish-NLP/gpt2-large-finnish |**30.74** |
|Finnish-NLP/gpt2-medium-finnish |34.08 |
|Finnish-NLP/gpt2-finnish |44.19 |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-large-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# GPT-2 medium for Finnish
Pretrained GPT-2 medium model on Finnish language using a causal language modeling (CLM) objective. GPT-2 was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
**Note**: this model is 345M parameter variant as in Huggingface's [GPT-2-medium config](https://huggingface.co/gpt2-medium), so not the famous big 1.5B parameter variant by OpenAI. We also have bigger 774M parameter variant [gpt2-large-finnish](https://huggingface.co/Finnish-NLP/gpt2-large-finnish) available which performs better compared to this model.
## Model description
Finnish GPT-2 is a transformers model pretrained on a very large corpus of Finnish data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation:
```python
>>> from transformers import pipeline
>>> generator = pipeline('text-generation', model='Finnish-NLP/gpt2-medium-finnish')
>>> generator("Tekstiä tuottava tekoäly on", max_length=30, num_return_sequences=5)
[{'generated_text': 'Tekstiä tuottava tekoäly on tullut ihmisten arkeen viime vuosina. Se auttaa hahmottamaan ja tulkitsemaan monimutkaisia kokonaisuuksia ja ilmiöitä, joita ihmiset tekevät esimerkiksi ruokakaupassa'},
{'generated_text': 'Tekstiä tuottava tekoäly on jo ottanut haltuunsa myös ihmisten käyttämiä sovelluksia ja esimerkiksi pankkipalveluita. Sen vuoksi tekoäly on tärkeä kumppani etenkin yritysten liiketoiminnan kehittämisessä.-'},
{'generated_text': 'Tekstiä tuottava tekoäly on tekoälylle luonnollinen valinta, sillä sen avulla voi kommunikoida ihmisten kanssa hyvin pitkälle samalla tavalla kuin tietokoneiden kanssa. Se on kehittynyt muun'},
{'generated_text': 'Tekstiä tuottava tekoäly on ihmisen kehittämä tekoäly, jota ei vielä ole pystytty rakentamaan. Tekoäly kykenee toimimaan esimerkiksi matemaattisissa, tilastollisissa ja sosiaalisissa'},
{'generated_text': 'Tekstiä tuottava tekoäly on jo niin iso juttu ettei sitä kannata rajoittaakaan. Ja jos se saadaan käyttöön, niin se voi jo pian syrjäyttää perinteisen'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-medium-finnish')
model = GPT2Model.from_pretrained('Finnish-NLP/gpt2-medium-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Finnish-NLP/gpt2-medium-finnish')
model = TFGPT2Model.from_pretrained('Finnish-NLP/gpt2-medium-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from neutral. Therefore, the model can have biased predictions. This bias will also affect all fine-tuned versions of this model.
As with all language models, it is hard to predict in advance how the Finnish GPT-2 will respond to particular prompts and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results.
## Training data
This Finnish GPT-2 model was pretrained on the combination of six datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive 2011-2018](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Yle Finnish News Archive 2019-2020](http://urn.fi/urn:nbn:fi:lb-2021050401)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 512 consecutive tokens.
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 360k steps (a bit over 1 epoch, 128 batch size). The optimizer used was a AdamW with learning rate 1e-4, learning rate warmup for 4000 steps and cosine decay of the learning rate after.
## Evaluation results
Evaluation was done using the *validation* split of the [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned) dataset with [Perplexity](https://huggingface.co/course/chapter7/3#perplexity-for-language-models) (smaller score the better) as the evaluation metric. As seen from the table below, this model (the first row of the table) performs better than our smaller [gpt2-finnish](https://huggingface.co/Finnish-NLP/gpt2-finnish) model variant but loses to our bigger [gpt2-large-finnish](https://huggingface.co/Finnish-NLP/gpt2-large-finnish) model.
| | Perplexity |
|------------------------------------------|------------|
|Finnish-NLP/gpt2-medium-finnish |34.08 |
|Finnish-NLP/gpt2-finnish |44.19 |
|Finnish-NLP/gpt2-large-finnish |**30.74** |
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen, [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "gpt2"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Teksti\u00e4 tuottava teko\u00e4ly on"}]} | Finnish-NLP/gpt2-medium-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# RoBERTa large model for Finnish
This **Finnish-NLP/roberta-large-finnish-v2** model is a new version of the previously trained [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model. Training hyperparameters were same but the training dataset was cleaned better with the goal to get better performing language model through the better cleaned data. Based on the model evaluations (check the table at the end), slightly better cleaned data didn't seem to produce better performing model.
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between finnish and Finnish.
## Model description
Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the RoBERTa model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-finnish-v2')
>>> unmasker("Moikka olen <mask> kielimalli.")
[{'score': 0.04741518571972847,
'token': 763,
'token_str': ' hyvä',
'sequence': 'Moikka olen hyvä kielimalli.'},
{'score': 0.036977022886276245,
'token': 505,
'token_str': ' myös',
'sequence': 'Moikka olen myös kielimalli.'},
{'score': 0.025283709168434143,
'token': 3089,
'token_str': ' huono',
'sequence': 'Moikka olen huono kielimalli.'},
{'score': 0.022848006337881088,
'token': 1852,
'token_str': ' toinen',
'sequence': 'Moikka olen toinen kielimalli.'},
{'score': 0.019232941791415215,
'token': 1029,
'token_str': ' siis',
'sequence': 'Moikka olen siis kielimalli.'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish-v2')
model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish-v2', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training data
This Finnish RoBERTa model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 520k train steps (2 epochs, batch size 512) with a sequence length of 128 and continuing for 520k steps (1 epoch, batch size 64) with a sequence length of 512. The optimizer used for the 128 sequence training was AdamW, and for the 512 sequence training it was Adafactor (to save memory). Learning rate was 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our previous [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|----------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |**94.90** |**95.49** |**76.07** |
To conclude, this model didn't significantly improve compared to our previous [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) model. This model is also slightly (~ 1%) losing to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-finnish-v2 | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# RoBERTa large model for Finnish
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta). This model is case-sensitive: it
makes a difference between finnish and Finnish.
## Model description
Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the RoBERTa model as inputs.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-finnish')
>>> unmasker("Moikka olen <mask> kielimalli.")
[{'sequence': 'Moikka olen hyvä kielimalli.',
'score': 0.1535797119140625,
'token': 767,
'token_str': ' hyvä'},
{'sequence': 'Moikka olen paras kielimalli.',
'score': 0.04795042425394058,
'token': 2888,
'token_str': ' paras'},
{'sequence': 'Moikka olen huono kielimalli.',
'score': 0.04251479730010033,
'token': 3217,
'token_str': ' huono'},
{'sequence': 'Moikka olen myös kielimalli.',
'score': 0.027469098567962646,
'token': 520,
'token_str': ' myös'},
{'sequence': 'Moikka olen se kielimalli.',
'score': 0.013878575526177883,
'token': 358,
'token_str': ' se'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish')
model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-finnish')
model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training data
This Finnish RoBERTa model was pretrained on the combination of five datasets:
- [mc4](https://huggingface.co/datasets/mc4), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 78GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 2 epochs with a sequence length of 128 and continuing for one more epoch with a sequence length of 512. The optimizer used is Adafactor with a learning rate of 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 1500 steps and linear decay of the learning rate after.
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) and to our previous [Finnish RoBERTa-large](https://huggingface.co/flax-community/RoBERTa-large-finnish) trained during the Hugging Face JAX/Flax community week:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|----------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |**94.90** |**95.49** |**76.07** |
|flax-community/RoBERTa-large-finnish |87.72 |94.42 |95.06 |73.67 |
To conclude, this model improves on our previous [Finnish RoBERTa-large](https://huggingface.co/flax-community/RoBERTa-large-finnish) model trained during the Hugging Face JAX/Flax community week but is still slightly (~ 1%) losing to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
- Tommi Vehviläinen [Hugging Face profile](https://huggingface.co/Tommi)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1907.11692",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
fill-mask | transformers |
# RoBERTa large model trained with WECHSEL method for Finnish
Pretrained RoBERTa model on Finnish language using a masked language modeling (MLM) objective with WECHSEL method. RoBERTa was introduced in
[this paper](https://arxiv.org/abs/1907.11692) and first released in
[this repository](https://github.com/pytorch/fairseq/tree/master/examples/roberta).
WECHSEL method (Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models) was introduced in [this paper](https://arxiv.org/abs/2112.06598) and first released in [this repository](https://github.com/CPJKU/wechsel).
This model is case-sensitive: it makes a difference between finnish and Finnish.
## Model description
Finnish RoBERTa is a transformers model pretrained on a large corpus of Finnish data in a self-supervised fashion. This means
it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of
publicly available data) with an automatic process to generate inputs and labels from those texts.
More precisely, it was pretrained with the Masked language modeling (MLM) objective. Taking a sentence, the model
randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict
the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one
after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to
learn a bidirectional representation of the sentence.
This way, the model learns an inner representation of the Finnish language that can then be used to extract features
useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard
classifier using the features produced by the RoBERTa model as inputs.
## WECHSEL method
Using the WECHSEL method, we first took the pretrained English [roberta-large](https://huggingface.co/roberta-large) model, changed its tokenizer with our Finnish tokenizer and initialized model's token embeddings such that they are close to semantically similar English tokens by utilizing multilingual static word embeddings (by fastText) covering English and Finnish. We were able to confirm the WECHSEL paper's findings that using this method you can save pretraining time and thus computing resources. To get idea of the WECHSEL method's training time savings you can check the table below illustrating the MLM evaluation accuracies during the pretraining compared to the [Finnish-NLP/roberta-large-finnish-v2](https://huggingface.co/Finnish-NLP/roberta-large-finnish-v2) which was trained from scratch:
| | 10k train steps | 100k train steps | 200k train steps | 270k train steps |
|------------------------------------------|------------------|------------------|------------------|------------------|
|Finnish-NLP/roberta-large-wechsel-finnish |37.61 eval acc |58.14 eval acc |61.60 eval acc |62.77 eval acc |
|Finnish-NLP/roberta-large-finnish-v2 |13.83 eval acc |55.87 eval acc |58.58 eval acc |59.47 eval acc |
Downstream finetuning text classification tests can be found from the end but there this model trained with WECHSEL method didn't significantly improve the downstream performances. However, based on tens of qualitative fill-mask task example tests we noticed that for fill-mask task this WECHSEL model significantly outperforms our other models trained from scratch.
## Intended uses & limitations
You can use the raw model for masked language modeling, but it's mostly intended to be fine-tuned on a downstream task.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='Finnish-NLP/roberta-large-wechsel-finnish')
>>> unmasker("Moikka olen <mask> kielimalli.")
[{'sequence': 'Moikka olen hyvä kielimalli.',
'score': 0.07757357507944107,
'token': 763,
'token_str': ' hyvä'},
{'sequence': 'Moikka olen suomen kielimalli.',
'score': 0.05297883599996567,
'token': 3641,
'token_str': ' suomen'},
{'sequence': 'Moikka olen kuin kielimalli.',
'score': 0.03747279942035675,
'token': 523,
'token_str': ' kuin'},
{'sequence': 'Moikka olen suomalainen kielimalli.',
'score': 0.031031042337417603,
'token': 4966,
'token_str': ' suomalainen'},
{'sequence': 'Moikka olen myös kielimalli.',
'score': 0.026489052921533585,
'token': 505,
'token_str': ' myös'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import RobertaTokenizer, RobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish')
model = RobertaModel.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import RobertaTokenizer, TFRobertaModel
tokenizer = RobertaTokenizer.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish')
model = TFRobertaModel.from_pretrained('Finnish-NLP/roberta-large-wechsel-finnish', from_pt=True)
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model contains a lot of unfiltered content from the internet, which is far from
neutral. Therefore, the model can have biased predictions.
## Training data
This Finnish RoBERTa model was pretrained on the combination of five datasets:
- [mc4_fi_cleaned](https://huggingface.co/datasets/Finnish-NLP/mc4_fi_cleaned), the dataset mC4 is a multilingual colossal, cleaned version of Common Crawl's web crawl corpus. We used the Finnish subset of the mC4 dataset and further cleaned it with our own text data cleaning codes (check the dataset repo).
- [wikipedia](https://huggingface.co/datasets/wikipedia) We used the Finnish subset of the wikipedia (August 2021) dataset
- [Yle Finnish News Archive](http://urn.fi/urn:nbn:fi:lb-2017070501)
- [Finnish News Agency Archive (STT)](http://urn.fi/urn:nbn:fi:lb-2018121001)
- [The Suomi24 Sentences Corpus](http://urn.fi/urn:nbn:fi:lb-2020021803)
Raw datasets were cleaned to filter out bad quality and non-Finnish examples. Together these cleaned datasets were around 84GB of text.
## Training procedure
### Preprocessing
The texts are tokenized using a byte version of Byte-Pair Encoding (BPE) and a vocabulary size of 50265. The inputs of
the model take pieces of 512 contiguous token that may span over documents. The beginning of a new document is marked
with `<s>` and the end of one by `</s>`
The details of the masking procedure for each sentence are the following:
- 15% of the tokens are masked.
- In 80% of the cases, the masked tokens are replaced by `<mask>`.
- In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
- In the 10% remaining cases, the masked tokens are left as is.
Contrary to BERT, the masking is done dynamically during pretraining (e.g., it changes at each epoch and is not fixed).
### Pretraining
The model was trained on TPUv3-8 VM, sponsored by the [Google TPU Research Cloud](https://sites.research.google/trc/about/), for 270k steps (a bit over 1 epoch, 512 batch size) with a sequence length of 128 and continuing for 180k steps (batch size 64) with a sequence length of 512. The optimizer used was Adafactor (to save memory). Learning rate was 2e-4, \\(\beta_{1} = 0.9\\), \\(\beta_{2} = 0.98\\) and \\(\epsilon = 1e-6\\), learning rate warmup for 2500 steps and linear decay of the learning rate after.
## Evaluation results
Evaluation was done by fine-tuning the model on downstream text classification task with two different labeled datasets: [Yle News](https://github.com/spyysalo/yle-corpus) and [Eduskunta](https://github.com/aajanki/eduskunta-vkk). Yle News classification fine-tuning was done with two different sequence lengths: 128 and 512 but Eduskunta only with 128 sequence length.
When fine-tuned on those datasets, this model (the first row of the table) achieves the following accuracy results compared to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model and to our previous [Finnish-NLP/roberta-large-finnish-v2](https://huggingface.co/Finnish-NLP/roberta-large-finnish-v2) and [Finnish-NLP/roberta-large-finnish](https://huggingface.co/Finnish-NLP/roberta-large-finnish) models:
| | Average | Yle News 128 length | Yle News 512 length | Eduskunta 128 length |
|------------------------------------------|----------|---------------------|---------------------|----------------------|
|Finnish-NLP/roberta-large-wechsel-finnish |88.19 |**94.91** |95.18 |74.47 |
|Finnish-NLP/roberta-large-finnish-v2 |88.17 |94.46 |95.22 |74.83 |
|Finnish-NLP/roberta-large-finnish |88.02 |94.53 |95.23 |74.30 |
|TurkuNLP/bert-base-finnish-cased-v1 |**88.82** |94.90 |**95.49** |**76.07** |
To conclude, this model didn't significantly improve compared to our previous models which were trained from scratch instead of using the WECHSEL method as in this model. This model is also slightly (~ 1%) losing to the [FinBERT (Finnish BERT)](https://huggingface.co/TurkuNLP/bert-base-finnish-cased-v1) model.
## Acknowledgements
This project would not have been possible without compute generously provided by Google through the
[TPU Research Cloud](https://sites.research.google/trc/).
## Team Members
- Aapo Tanskanen, [Hugging Face profile](https://huggingface.co/aapot), [LinkedIn profile](https://www.linkedin.com/in/aapotanskanen/)
- Rasmus Toivanen [Hugging Face profile](https://huggingface.co/RASMUS), [LinkedIn profile](https://www.linkedin.com/in/rasmustoivanen/)
Feel free to contact us for more details 🤗 | {"language": ["fi"], "license": "apache-2.0", "tags": ["finnish", "roberta"], "datasets": ["Finnish-NLP/mc4_fi_cleaned", "wikipedia"], "widget": [{"text": "Moikka olen <mask> kielimalli."}]} | Finnish-NLP/roberta-large-wechsel-finnish | null | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"finnish",
"fi",
"dataset:Finnish-NLP/mc4_fi_cleaned",
"dataset:wikipedia",
"arxiv:1907.11692",
"arxiv:2112.06598",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-classification | transformers | {} | Fiona99/distilbert-base-uncased-finetuned-cola | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-v2-finetuned-squad
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9901
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8584 | 1.0 | 5540 | 0.9056 |
| 0.6473 | 2.0 | 11080 | 0.8975 |
| 0.4801 | 3.0 | 16620 | 0.9901 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "albert-base-v2-finetuned-squad", "results": []}]} | Firat/albert-base-v2-finetuned-squad | null | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1460
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.2856 | 1.0 | 2767 | 1.1919 |
| 1.012 | 2.0 | 5534 | 1.1332 |
| 0.8512 | 3.0 | 8301 | 1.1460 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.18.0
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "distilbert-base-uncased-finetuned-squad", "results": []}]} | Firat/distilbert-base-uncased-finetuned-squad | null | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-squad
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8926 | 1.0 | 5536 | 0.8694 |
| 0.6821 | 2.0 | 11072 | 0.8428 |
| 0.5335 | 3.0 | 16608 | 0.8953 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1
- Datasets 1.17.0
- Tokenizers 0.10.3
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "roberta-base-finetuned-squad", "results": []}]} | Firat/roberta-base-finetuned-squad | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Firestawn/Ru | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
fill-mask | transformers | {} | FirmanBr/FirmanBrilianBert | null | [
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers | {} | FirmanBr/FirmanIndoLanguageModel | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers | {} | FirmanBr/chibibot | null | [
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {"license": "gpl"} | FisherYu/test_code_nlp | null | [
"license:gpl",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-guarani-colab
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2392
- Wer: 1.0743
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 18.2131 | 49.94 | 400 | 3.2901 | 1.0 |
| 2.0496 | 99.94 | 800 | 3.2392 | 1.0743 |
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "wav2vec2-large-xls-r-300m-guarani-colab", "results": []}]} | FitoDS/wav2vec2-large-xls-r-300m-guarani-colab | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
automatic-speech-recognition | transformers | {} | FitoDS/wav2vec2-large-xls-r-300m-spanish-large | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FitoDS/wav2vec2-large-xls-r-300m-turkish-colab | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
#
This model is a fine-tuned version of [hf-test/xls-r-dummy](https://huggingface.co/hf-test/xls-r-dummy) on the COMMON_VOICE - AB dataset.
It achieves the following results on the evaluation set:
- Loss: 133.5167
- Wer: 18.9286
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.1+cu102
- Datasets 1.17.1.dev0
- Tokenizers 0.11.0
| {"language": ["ab"], "tags": ["automatic-speech-recognition", "common_voice", "generated_from_trainer"], "datasets": ["common_voice"], "model-index": [{"name": "", "results": []}]} | FitoDS/xls-r-ab-test | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"common_voice",
"generated_from_trainer",
"ab",
"dataset:common_voice",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Flakko/FlakkoDaniel | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers |
# Sheldon Cooper from The Big Bang Theory Show DialoGPT Model | {"tags": ["conversational"]} | Flampt/DialoGPT-medium-Sheldon | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Flarrix/gpt11-lmao | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FloKit/bert-base-uncased-finetuned-squad | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FloZe92/DialoGPT-small-harrypotter_ | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Flyguy/model_name | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | FlyingFrog/Model1 | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | #
| {"tags": ["conversational"]} | For/sheldonbot | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | null | {} | Forax/For | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
text-generation | transformers | {} | Forest/gpt2-fanfic | null | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
null | null | {} | Forost/Out | null | [
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-fa-QA-v1
Persian Question and answer Model Based on Bert Model
This model is a fine-tuned version of [ParsBERT](https://arxiv.org/abs/2005.12515) on PersianQA dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7297
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2563 | 1.0 | 1126 | 1.7222 |
| 1.3372 | 2.0 | 2252 | 1.7297 |
### Framework versions
- Transformers 4.9.0
- Pytorch 1.9.0+cu102
- Tokenizers 0.10.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model_index": [{"name": "bert-fa-QA-v1", "results": [{"task": {"name": "Question Answering", "type": "question-answering"}}]}]} | ForutanRad/bert-fa-QA-v1 | null | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"arxiv:2005.12515",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
text-generation | transformers |
# Chat Bot Test | {"tags": ["conversational"]} | FosterPatch/GoT-test | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
null | transformers | {} | Francesco/dummy | null | [
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers | {} | Francesco/resnet101-224-1k | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers | {} | Francesco/resnet101 | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers | {} | Francesco/resnet152-224-1k | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers | {} | Francesco/resnet152 | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
|
image-classification | transformers | {} | Francesco/resnet18-224-1k | null | [
"transformers",
"pytorch",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04+00:00 |
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